Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations32401
Missing cells596
Missing cells (%)0.1%
Duplicate rows5390
Duplicate rows (%)16.6%
Total size in memory28.4 MiB
Average record size in memory918.4 B

Variable types

DateTime1
Categorical8
Text3
Unsupported1
Numeric12

Alerts

Hub has constant value "Kolkata" Constant
Category has constant value "AC" Constant
Dataset has 5390 (16.6%) duplicate rowsDuplicates
Agent Amount is highly overall correlated with Agent Commission and 5 other fieldsHigh correlation
Agent Commission is highly overall correlated with Agent Amount and 4 other fieldsHigh correlation
Agents is highly overall correlated with Agent Amount and 5 other fieldsHigh correlation
Amount is highly overall correlated with Agent Amount and 5 other fieldsHigh correlation
Base Fare is highly overall correlated with Agent Amount and 5 other fieldsHigh correlation
Coach No is highly overall correlated with Coach Type and 1 other fieldsHigh correlation
Coach Type is highly overall correlated with Coach No and 2 other fieldsHigh correlation
E Ticket is highly overall correlated with E Ticket AmountHigh correlation
E Ticket Amount is highly overall correlated with E TicketHigh correlation
Net Amount is highly overall correlated with Agent Amount and 5 other fieldsHigh correlation
Origin is highly overall correlated with Place Of SupplyHigh correlation
Place Of Supply is highly overall correlated with OriginHigh correlation
Route is highly overall correlated with Coach Type and 1 other fieldsHigh correlation
Seating Capacity is highly overall correlated with Coach No and 2 other fieldsHigh correlation
Seats is highly overall correlated with Agent Amount and 4 other fieldsHigh correlation
Coach No has 596 (1.8%) missing values Missing
Cash Amount is highly skewed (γ1 = 39.87153405) Skewed
E Ticket Amount is highly skewed (γ1 = 31.35661321) Skewed
Dept Time is an unsupported type, check if it needs cleaning or further analysis Unsupported
E Ticket has 31468 (97.1%) zeros Zeros
Agents has 1268 (3.9%) zeros Zeros
Cash Amount has 32066 (99.0%) zeros Zeros
E Ticket Amount has 31471 (97.1%) zeros Zeros
Agent Amount has 1299 (4.0%) zeros Zeros
GST has 23897 (73.8%) zeros Zeros
Agent Commission has 1299 (4.0%) zeros Zeros

Reproduction

Analysis started2025-05-20 06:28:51.795256
Analysis finished2025-05-20 06:29:17.452807
Duration25.66 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Doj
Date

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size253.3 KiB
Minimum2023-01-01 00:00:00
Maximum2023-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-20T06:29:17.566952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:17.701199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

Hub
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Kolkata
32401 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters226807
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata
2nd rowKolkata
3rd rowKolkata
4th rowKolkata
5th rowKolkata

Common Values

ValueCountFrequency (%)
Kolkata 32401
100.0%

Length

2025-05-20T06:29:17.835404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T06:29:17.919492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 32401
100.0%

Most occurring characters

ValueCountFrequency (%)
a 64802
28.6%
K 32401
14.3%
o 32401
14.3%
l 32401
14.3%
k 32401
14.3%
t 32401
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 194406
85.7%
Uppercase Letter 32401
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 64802
33.3%
o 32401
16.7%
l 32401
16.7%
k 32401
16.7%
t 32401
16.7%
Uppercase Letter
ValueCountFrequency (%)
K 32401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 226807
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 64802
28.6%
K 32401
14.3%
o 32401
14.3%
l 32401
14.3%
k 32401
14.3%
t 32401
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 64802
28.6%
K 32401
14.3%
o 32401
14.3%
l 32401
14.3%
k 32401
14.3%
t 32401
14.3%

Route
Categorical

High correlation 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Asansol - Kolkata (Karunamoyee)
5148 
Asansol To Kolkata
3744 
Kolkata (Karunamoyee) - Asansol
3307 
Kolkata To Asansol
 
1651
Kolkata (Karunamoyee) To Asansol
 
1198
Other values (32)
17353 

Length

Max length37
Median length31
Mean length23.537885
Min length14

Characters and Unicode

Total characters762651
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsansol - Kolkata (Karunamoyee)
2nd rowAsansol - Kolkata (Karunamoyee)
3rd rowAsansol - Kolkata (Karunamoyee)
4th rowAsansol - Kolkata (Karunamoyee)
5th rowAsansol - Kolkata (Karunamoyee)

Common Values

ValueCountFrequency (%)
Asansol - Kolkata (Karunamoyee) 5148
15.9%
Asansol To Kolkata 3744
 
11.6%
Kolkata (Karunamoyee) - Asansol 3307
 
10.2%
Kolkata To Asansol 1651
 
5.1%
Kolkata (Karunamoyee) To Asansol 1198
 
3.7%
Asansol - Puri - 1301 1087
 
3.4%
Bokaro To Kolkata 1081
 
3.3%
Sliguri - Kolkata 1006
 
3.1%
Hili To Kolkata - 1602 926
 
2.9%
Kolkata - Siliguri - 907 840
 
2.6%
Other values (27) 12413
38.3%

Length

2025-05-20T06:29:18.009504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kolkata 29179
23.1%
27362
21.7%
asansol 19730
15.7%
to 13676
10.8%
karunamoyee 10359
 
8.2%
puri 4210
 
3.3%
siliguri 2629
 
2.1%
sliguri 2399
 
1.9%
bokaro 1886
 
1.5%
hili 1640
 
1.3%
Other values (21) 12982
10.3%

Most occurring characters

ValueCountFrequency (%)
a 105007
13.8%
93651
12.3%
o 77304
 
10.1%
l 57697
 
7.6%
K 40764
 
5.3%
s 39067
 
5.1%
k 31635
 
4.1%
t 31608
 
4.1%
n 31162
 
4.1%
- 27362
 
3.6%
Other values (38) 227394
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 494995
64.9%
Space Separator 93651
 
12.3%
Uppercase Letter 92450
 
12.1%
Decimal Number 32335
 
4.2%
Dash Punctuation 27362
 
3.6%
Open Punctuation 10929
 
1.4%
Close Punctuation 10929
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 105007
21.2%
o 77304
15.6%
l 57697
11.7%
s 39067
 
7.9%
k 31635
 
6.4%
t 31608
 
6.4%
n 31162
 
6.3%
e 23755
 
4.8%
r 22547
 
4.6%
i 22147
 
4.5%
Other values (9) 53066
10.7%
Uppercase Letter
ValueCountFrequency (%)
K 40764
44.1%
A 20221
21.9%
T 11903
 
12.9%
S 6307
 
6.8%
P 4210
 
4.6%
B 1886
 
2.0%
H 1726
 
1.9%
D 1618
 
1.8%
R 896
 
1.0%
I 785
 
0.8%
Other values (6) 2134
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 11216
34.7%
0 10030
31.0%
9 2213
 
6.8%
2 1934
 
6.0%
3 1695
 
5.2%
7 1456
 
4.5%
5 1455
 
4.5%
4 1410
 
4.4%
6 926
 
2.9%
Space Separator
ValueCountFrequency (%)
93651
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27362
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10929
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 587445
77.0%
Common 175206
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 105007
17.9%
o 77304
13.2%
l 57697
9.8%
K 40764
 
6.9%
s 39067
 
6.7%
k 31635
 
5.4%
t 31608
 
5.4%
n 31162
 
5.3%
e 23755
 
4.0%
r 22547
 
3.8%
Other values (25) 126899
21.6%
Common
ValueCountFrequency (%)
93651
53.5%
- 27362
 
15.6%
1 11216
 
6.4%
( 10929
 
6.2%
) 10929
 
6.2%
0 10030
 
5.7%
9 2213
 
1.3%
2 1934
 
1.1%
3 1695
 
1.0%
7 1456
 
0.8%
Other values (3) 3791
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 762651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105007
13.8%
93651
12.3%
o 77304
 
10.1%
l 57697
 
7.6%
K 40764
 
5.3%
s 39067
 
5.1%
k 31635
 
4.1%
t 31608
 
4.1%
n 31162
 
4.1%
- 27362
 
3.6%
Other values (38) 227394
29.8%
Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2025-05-20T06:29:18.258000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.1256751
Min length6

Characters and Unicode

Total characters263280
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS - 401
2nd rowAS - 401
3rd rowAS - 401
4th rowAS - 401
5th rowAS - 401
ValueCountFrequency (%)
28289
31.6%
as 5854
 
6.5%
sa 5075
 
5.7%
ae 4332
 
4.8%
sk 2210
 
2.5%
ea 1976
 
2.2%
ks 1492
 
1.7%
bk 1081
 
1.2%
601 1081
 
1.2%
pa 990
 
1.1%
Other values (87) 37034
41.4%
2025-05-20T06:29:18.601190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57175
21.7%
0 35408
13.4%
- 32401
12.3%
1 22090
 
8.4%
A 19730
 
7.5%
S 16162
 
6.1%
2 12603
 
4.8%
K 12585
 
4.8%
3 8923
 
3.4%
4 8720
 
3.3%
Other values (22) 37483
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107066
40.7%
Uppercase Letter 66470
25.2%
Space Separator 57175
21.7%
Dash Punctuation 32401
 
12.3%
Lowercase Letter 168
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19730
29.7%
S 16162
24.3%
K 12585
18.9%
E 6308
 
9.5%
P 4626
 
7.0%
B 1886
 
2.8%
D 1758
 
2.6%
H 1640
 
2.5%
R 663
 
1.0%
N 416
 
0.6%
Other values (3) 696
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 35408
33.1%
1 22090
20.6%
2 12603
 
11.8%
3 8923
 
8.3%
4 8720
 
8.1%
5 4696
 
4.4%
7 4312
 
4.0%
6 4121
 
3.8%
9 3732
 
3.5%
8 2461
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
s 24
14.3%
p 24
14.3%
e 24
14.3%
c 24
14.3%
i 24
14.3%
a 24
14.3%
l 24
14.3%
Space Separator
ValueCountFrequency (%)
57175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196642
74.7%
Latin 66638
 
25.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19730
29.6%
S 16162
24.3%
K 12585
18.9%
E 6308
 
9.5%
P 4626
 
6.9%
B 1886
 
2.8%
D 1758
 
2.6%
H 1640
 
2.5%
R 663
 
1.0%
N 416
 
0.6%
Other values (10) 864
 
1.3%
Common
ValueCountFrequency (%)
57175
29.1%
0 35408
18.0%
- 32401
16.5%
1 22090
 
11.2%
2 12603
 
6.4%
3 8923
 
4.5%
4 8720
 
4.4%
5 4696
 
2.4%
7 4312
 
2.2%
6 4121
 
2.1%
Other values (2) 6193
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57175
21.7%
0 35408
13.4%
- 32401
12.3%
1 22090
 
8.4%
A 19730
 
7.5%
S 16162
 
6.1%
2 12603
 
4.8%
K 12585
 
4.8%
3 8923
 
3.4%
4 8720
 
3.3%
Other values (22) 37483
14.2%

Origin
Categorical

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Kolkata
11145 
Durgapur ( West Bengal )
6209 
Asansol
4163 
Siliguri
2519 
Saltlake
1662 
Other values (26)
6703 

Length

Max length24
Median length7
Mean length10.519212
Min length4

Characters and Unicode

Total characters340833
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDurgapur ( West Bengal )
2nd rowBurdwan
3rd rowDurgapur ( West Bengal )
4th rowDurgapur ( West Bengal )
5th rowDurgapur ( West Bengal )

Common Values

ValueCountFrequency (%)
Kolkata 11145
34.4%
Durgapur ( West Bengal ) 6209
19.2%
Asansol 4163
 
12.8%
Siliguri 2519
 
7.8%
Saltlake 1662
 
5.1%
Bhubaneswar 1206
 
3.7%
Raniganj 653
 
2.0%
Panagarh 588
 
1.8%
Bokaro 559
 
1.7%
Puri 530
 
1.6%
Other values (21) 3167
 
9.8%

Length

2025-05-20T06:29:18.742331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12418
21.7%
kolkata 11145
19.4%
bengal 6241
10.9%
west 6241
10.9%
durgapur 6210
10.8%
asansol 4163
 
7.3%
siliguri 2519
 
4.4%
saltlake 1662
 
2.9%
bhubaneswar 1206
 
2.1%
raniganj 653
 
1.1%
Other values (24) 4881
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 53199
15.6%
l 28198
 
8.3%
24938
 
7.3%
t 20081
 
5.9%
r 19251
 
5.6%
u 18032
 
5.3%
g 17211
 
5.0%
o 16444
 
4.8%
s 15773
 
4.6%
e 15350
 
4.5%
Other values (27) 112356
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 258480
75.8%
Uppercase Letter 44857
 
13.2%
Space Separator 24938
 
7.3%
Close Punctuation 6279
 
1.8%
Open Punctuation 6279
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53199
20.6%
l 28198
10.9%
t 20081
 
7.8%
r 19251
 
7.4%
u 18032
 
7.0%
g 17211
 
6.7%
o 16444
 
6.4%
s 15773
 
6.1%
e 15350
 
5.9%
n 15149
 
5.9%
Other values (10) 39792
15.4%
Uppercase Letter
ValueCountFrequency (%)
K 11177
24.9%
B 8831
19.7%
D 7106
15.8%
W 6209
13.8%
S 4188
 
9.3%
A 4163
 
9.3%
P 1118
 
2.5%
R 835
 
1.9%
M 527
 
1.2%
C 358
 
0.8%
Other values (4) 345
 
0.8%
Space Separator
ValueCountFrequency (%)
24938
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6279
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 303337
89.0%
Common 37496
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53199
17.5%
l 28198
 
9.3%
t 20081
 
6.6%
r 19251
 
6.3%
u 18032
 
5.9%
g 17211
 
5.7%
o 16444
 
5.4%
s 15773
 
5.2%
e 15350
 
5.1%
n 15149
 
5.0%
Other values (24) 84649
27.9%
Common
ValueCountFrequency (%)
24938
66.5%
) 6279
 
16.7%
( 6279
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 53199
15.6%
l 28198
 
8.3%
24938
 
7.3%
t 20081
 
5.9%
r 19251
 
5.6%
u 18032
 
5.3%
g 17211
 
5.0%
o 16444
 
4.8%
s 15773
 
4.6%
e 15350
 
4.5%
Other values (27) 112356
33.0%

Destination
Categorical

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Kolkata
15954 
Durgapur ( West Bengal )
4898 
Asansol
2734 
Siliguri
2516 
Bhubaneswar
 
1254
Other values (22)
5045 

Length

Max length24
Median length7
Mean length9.7396994
Min length4

Characters and Unicode

Total characters315576
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata
2nd rowSaltlake
3rd rowKolkata
4th rowKolkata
5th rowKolkata

Common Values

ValueCountFrequency (%)
Kolkata 15954
49.2%
Durgapur ( West Bengal ) 4898
 
15.1%
Asansol 2734
 
8.4%
Siliguri 2516
 
7.8%
Bhubaneswar 1254
 
3.9%
Burdwan 691
 
2.1%
Digha 684
 
2.1%
Puri 565
 
1.7%
Saltlake 442
 
1.4%
Bokaro 427
 
1.3%
Other values (17) 2236
 
6.9%

Length

2025-05-20T06:29:18.893390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kolkata 15954
30.7%
9796
18.8%
durgapur 4898
 
9.4%
west 4898
 
9.4%
bengal 4898
 
9.4%
asansol 2734
 
5.3%
siliguri 2516
 
4.8%
bhubaneswar 1254
 
2.4%
burdwan 691
 
1.3%
digha 684
 
1.3%
Other values (21) 3692
 
7.1%

Most occurring characters

ValueCountFrequency (%)
a 53636
17.0%
l 27633
 
8.8%
t 22199
 
7.0%
19614
 
6.2%
o 19558
 
6.2%
k 17262
 
5.5%
r 16101
 
5.1%
K 15954
 
5.1%
u 15557
 
4.9%
g 13892
 
4.4%
Other values (27) 94170
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 243903
77.3%
Uppercase Letter 42219
 
13.4%
Space Separator 19614
 
6.2%
Close Punctuation 4920
 
1.6%
Open Punctuation 4920
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53636
22.0%
l 27633
11.3%
t 22199
9.1%
o 19558
 
8.0%
k 17262
 
7.1%
r 16101
 
6.6%
u 15557
 
6.4%
g 13892
 
5.7%
s 11620
 
4.8%
e 11492
 
4.7%
Other values (10) 34953
14.3%
Uppercase Letter
ValueCountFrequency (%)
K 15954
37.8%
B 7625
18.1%
D 5788
 
13.7%
W 4898
 
11.6%
S 2958
 
7.0%
A 2734
 
6.5%
P 700
 
1.7%
R 600
 
1.4%
M 338
 
0.8%
C 315
 
0.7%
Other values (4) 309
 
0.7%
Space Separator
ValueCountFrequency (%)
19614
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4920
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 286122
90.7%
Common 29454
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53636
18.7%
l 27633
 
9.7%
t 22199
 
7.8%
o 19558
 
6.8%
k 17262
 
6.0%
r 16101
 
5.6%
K 15954
 
5.6%
u 15557
 
5.4%
g 13892
 
4.9%
s 11620
 
4.1%
Other values (24) 72710
25.4%
Common
ValueCountFrequency (%)
19614
66.6%
) 4920
 
16.7%
( 4920
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 53636
17.0%
l 27633
 
8.8%
t 22199
 
7.0%
19614
 
6.2%
o 19558
 
6.2%
k 17262
 
5.5%
r 16101
 
5.1%
K 15954
 
5.1%
u 15557
 
4.9%
g 13892
 
4.4%
Other values (27) 94170
29.8%

Place Of Supply
Categorical

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Kolkata - West Bengal
11145 
Durgapur ( West Bengal ) - West Bengal
6209 
Asansol - West Bengal
4163 
Siliguri - West Bengal
2519 
Saltlake - West Bengal
1662 
Other values (26)
6703 

Length

Max length38
Median length21
Mean length24.234592
Min length14

Characters and Unicode

Total characters785225
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDurgapur ( West Bengal ) - West Bengal
2nd rowBurdwan - West Bengal
3rd rowDurgapur ( West Bengal ) - West Bengal
4th rowDurgapur ( West Bengal ) - West Bengal
5th rowDurgapur ( West Bengal ) - West Bengal

Common Values

ValueCountFrequency (%)
Kolkata - West Bengal 11145
34.4%
Durgapur ( West Bengal ) - West Bengal 6209
19.2%
Asansol - West Bengal 4163
 
12.8%
Siliguri - West Bengal 2519
 
7.8%
Saltlake - West Bengal 1662
 
5.1%
Bhubaneswar - Orissa 1206
 
3.7%
Raniganj - Uttar Pradesh 653
 
2.0%
Panagarh - West Bengal 588
 
1.8%
Bokaro - Jharkhand 559
 
1.7%
Puri - Orissa 530
 
1.6%
Other values (21) 3167
 
9.8%

Length

2025-05-20T06:29:19.016275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
44819
29.6%
bengal 34809
23.0%
west 34809
23.0%
kolkata 11145
 
7.4%
durgapur 6210
 
4.1%
asansol 4163
 
2.8%
siliguri 2519
 
1.7%
orissa 2084
 
1.4%
saltlake 1662
 
1.1%
bhubaneswar 1206
 
0.8%
Other values (27) 7936
 
5.2%

Most occurring characters

ValueCountFrequency (%)
121045
15.4%
a 87349
11.1%
e 73139
 
9.3%
l 56766
 
7.2%
t 49955
 
6.4%
s 49162
 
6.3%
g 45779
 
5.8%
n 44813
 
5.7%
B 37399
 
4.8%
W 34777
 
4.4%
Other values (30) 185041
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 512742
65.3%
Space Separator 121045
 
15.4%
Uppercase Letter 106479
 
13.6%
Dash Punctuation 32401
 
4.1%
Open Punctuation 6279
 
0.8%
Close Punctuation 6279
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 87349
17.0%
e 73139
14.3%
l 56766
11.1%
t 49955
9.7%
s 49162
9.6%
g 45779
8.9%
n 44813
8.7%
r 23737
 
4.6%
u 18032
 
3.5%
o 16444
 
3.2%
Other values (10) 47566
9.3%
Uppercase Letter
ValueCountFrequency (%)
B 37399
35.1%
W 34777
32.7%
K 11177
 
10.5%
D 7106
 
6.7%
S 4188
 
3.9%
A 4163
 
3.9%
O 2084
 
2.0%
P 1771
 
1.7%
J 1134
 
1.1%
R 835
 
0.8%
Other values (6) 1845
 
1.7%
Space Separator
ValueCountFrequency (%)
121045
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32401
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6279
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 619221
78.9%
Common 166004
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 87349
14.1%
e 73139
11.8%
l 56766
9.2%
t 49955
8.1%
s 49162
7.9%
g 45779
 
7.4%
n 44813
 
7.2%
B 37399
 
6.0%
W 34777
 
5.6%
r 23737
 
3.8%
Other values (26) 116345
18.8%
Common
ValueCountFrequency (%)
121045
72.9%
- 32401
 
19.5%
( 6279
 
3.8%
) 6279
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 785225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121045
15.4%
a 87349
11.1%
e 73139
 
9.3%
l 56766
 
7.2%
t 49955
 
6.4%
s 49162
 
6.3%
g 45779
 
5.8%
n 44813
 
5.7%
B 37399
 
4.8%
W 34777
 
4.4%
Other values (30) 185041
23.6%

Dept Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.5 MiB

Coach No
Categorical

High correlation  Missing 

Distinct44
Distinct (%)0.1%
Missing596
Missing (%)1.8%
Memory size2.1 MiB
WB 23 E 3888
 
1886
WB 37 C 8084
 
1180
WB 23 E 3411
 
1122
WB 23 E 3890
 
1046
WB 23 D 7857
 
1034
Other values (39)
25537 

Length

Max length12
Median length12
Mean length11.999088
Min length11

Characters and Unicode

Total characters381631
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWB 37 C 3388
2nd rowWB 37 C 3388
3rd rowWB 37 C 3388
4th rowWB 37 C 3388
5th rowWB 37 C 3388

Common Values

ValueCountFrequency (%)
WB 23 E 3888 1886
 
5.8%
WB 37 C 8084 1180
 
3.6%
WB 23 E 3411 1122
 
3.5%
WB 23 E 3890 1046
 
3.2%
WB 23 D 7857 1034
 
3.2%
WB 37 D 0098 1033
 
3.2%
WB 37 C 5597 987
 
3.0%
WB 73 G 3667 938
 
2.9%
WB 41 F 0307 922
 
2.8%
WB 37 D 0602 903
 
2.8%
Other values (34) 20754
64.1%

Length

2025-05-20T06:29:19.129553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wb 31235
24.6%
23 13838
10.9%
e 12238
 
9.6%
37 11302
 
8.9%
d 8202
 
6.4%
c 4700
 
3.7%
73 4690
 
3.7%
f 3786
 
3.0%
3888 1886
 
1.5%
g 1826
 
1.4%
Other values (49) 33488
26.3%

Most occurring characters

ValueCountFrequency (%)
95386
25.0%
3 42370
11.1%
B 31805
 
8.3%
W 31235
 
8.2%
7 29842
 
7.8%
0 21778
 
5.7%
2 20740
 
5.4%
5 15280
 
4.0%
8 15249
 
4.0%
6 13778
 
3.6%
Other values (11) 64168
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190830
50.0%
Uppercase Letter 95415
25.0%
Space Separator 95386
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 42370
22.2%
7 29842
15.6%
0 21778
11.4%
2 20740
10.9%
5 15280
 
8.0%
8 15249
 
8.0%
6 13778
 
7.2%
1 10845
 
5.7%
9 10715
 
5.6%
4 10233
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 31805
33.3%
W 31235
32.7%
E 12267
 
12.9%
D 8202
 
8.6%
C 4700
 
4.9%
F 3786
 
4.0%
G 1826
 
1.9%
N 570
 
0.6%
L 570
 
0.6%
A 454
 
0.5%
Space Separator
ValueCountFrequency (%)
95386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 286216
75.0%
Latin 95415
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
95386
33.3%
3 42370
14.8%
7 29842
 
10.4%
0 21778
 
7.6%
2 20740
 
7.2%
5 15280
 
5.3%
8 15249
 
5.3%
6 13778
 
4.8%
1 10845
 
3.8%
9 10715
 
3.7%
Latin
ValueCountFrequency (%)
B 31805
33.3%
W 31235
32.7%
E 12267
 
12.9%
D 8202
 
8.6%
C 4700
 
4.9%
F 3786
 
4.0%
G 1826
 
1.9%
N 570
 
0.6%
L 570
 
0.6%
A 454
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95386
25.0%
3 42370
11.1%
B 31805
 
8.3%
W 31235
 
8.2%
7 29842
 
7.8%
0 21778
 
5.7%
2 20740
 
5.4%
5 15280
 
4.0%
8 15249
 
4.0%
6 13778
 
3.6%
Other values (11) 64168
16.8%

Coach Type
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Volvo Seater
16889 
Multi Axle Volvo SemiSleeper
4864 
VOLVO MULTI AXLE B11R Sleeper /Seater ( 2+1 )
3450 
Scania Multi-Axle Semi Sleeper
2033 
VOLVO B11R I-SHIFT MULTI AXEL SEATER
 
1538
Other values (4)
3627 

Length

Max length47
Median length12
Mean length22.581618
Min length12

Characters and Unicode

Total characters731667
Distinct characters43
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVolvo Seater
2nd rowVolvo Seater
3rd rowVolvo Seater
4th rowVolvo Seater
5th rowVolvo Seater

Common Values

ValueCountFrequency (%)
Volvo Seater 16889
52.1%
Multi Axle Volvo SemiSleeper 4864
 
15.0%
VOLVO MULTI AXLE B11R Sleeper /Seater ( 2+1 ) 3450
 
10.6%
Scania Multi-Axle Semi Sleeper 2033
 
6.3%
VOLVO B11R I-SHIFT MULTI AXEL SEATER 1538
 
4.7%
Scania Metro Link 1245
 
3.8%
Volvo Multi Axle I-Shift B11R Full Sleeper 1240
 
3.8%
Volvo AC Seater Executive Luxury 2+2 1113
 
3.4%
Volvo B11R multi axle semi sleeper 29
 
0.1%

Length

2025-05-20T06:29:19.250294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T06:29:19.422927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
volvo 29123
24.1%
seater 22990
19.0%
multi 11121
 
9.2%
axle 9583
 
7.9%
6900
 
5.7%
sleeper 6752
 
5.6%
b11r 6257
 
5.2%
semisleeper 4864
 
4.0%
2+1 3450
 
2.9%
scania 3278
 
2.7%
Other values (11) 16593
13.7%

Most occurring characters

ValueCountFrequency (%)
e 96315
13.2%
95410
13.0%
l 54563
 
7.5%
o 49515
 
6.8%
S 47530
 
6.5%
r 35426
 
4.8%
V 34111
 
4.7%
t 33216
 
4.5%
a 28037
 
3.8%
v 25248
 
3.5%
Other values (33) 232296
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 399806
54.6%
Uppercase Letter 195087
26.7%
Space Separator 95410
 
13.0%
Decimal Number 21640
 
3.0%
Dash Punctuation 4811
 
0.7%
Math Symbol 4563
 
0.6%
Open Punctuation 3450
 
0.5%
Other Punctuation 3450
 
0.5%
Close Punctuation 3450
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 96315
24.1%
l 54563
13.6%
o 49515
12.4%
r 35426
 
8.9%
t 33216
 
8.3%
a 28037
 
7.0%
v 25248
 
6.3%
i 21968
 
5.5%
u 12745
 
3.2%
p 11616
 
2.9%
Other values (9) 31157
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
S 47530
24.4%
V 34111
17.5%
L 17322
 
8.9%
A 15776
 
8.1%
M 14370
 
7.4%
O 9976
 
5.1%
I 9304
 
4.8%
E 9177
 
4.7%
T 8064
 
4.1%
R 7795
 
4.0%
Other values (6) 21662
11.1%
Decimal Number
ValueCountFrequency (%)
1 15964
73.8%
2 5676
 
26.2%
Space Separator
ValueCountFrequency (%)
95410
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4811
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4563
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3450
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3450
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 594893
81.3%
Common 136774
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 96315
16.2%
l 54563
 
9.2%
o 49515
 
8.3%
S 47530
 
8.0%
r 35426
 
6.0%
V 34111
 
5.7%
t 33216
 
5.6%
a 28037
 
4.7%
v 25248
 
4.2%
i 21968
 
3.7%
Other values (25) 168964
28.4%
Common
ValueCountFrequency (%)
95410
69.8%
1 15964
 
11.7%
2 5676
 
4.1%
- 4811
 
3.5%
+ 4563
 
3.3%
( 3450
 
2.5%
/ 3450
 
2.5%
) 3450
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 96315
13.2%
95410
13.0%
l 54563
 
7.5%
o 49515
 
6.8%
S 47530
 
6.5%
r 35426
 
4.8%
V 34111
 
4.7%
t 33216
 
4.5%
a 28037
 
3.8%
v 25248
 
3.5%
Other values (33) 232296
31.7%

Category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
AC
32401 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters64802
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAC
3rd rowAC
4th rowAC
5th rowAC

Common Values

ValueCountFrequency (%)
AC 32401
100.0%

Length

2025-05-20T06:29:19.653715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T06:29:19.753833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ac 32401
100.0%

Most occurring characters

ValueCountFrequency (%)
A 32401
50.0%
C 32401
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 64802
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32401
50.0%
C 32401
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64802
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32401
50.0%
C 32401
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 32401
50.0%
C 32401
50.0%
Distinct134
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-05-20T06:29:20.119687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length7
Mean length9.3763155
Min length5

Characters and Unicode

Total characters303802
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowRedbus
2nd rowRedbus
3rd rowBhairab Singha
4th rowRedbus
5th rowRedbus
ValueCountFrequency (%)
redbus 21931
51.9%
shyamoli 1965
 
4.7%
paytm 996
 
2.4%
administrator 933
 
2.2%
system 933
 
2.2%
asansol 824
 
1.9%
bikram 688
 
1.6%
durgapur 688
 
1.6%
abhibus 606
 
1.4%
karunamoye 599
 
1.4%
Other values (195) 12094
28.6%
2025-05-20T06:29:20.730513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34102
11.2%
u 28205
 
9.3%
s 27729
 
9.1%
b 25711
 
8.5%
e 24657
 
8.1%
d 23484
 
7.7%
R 22729
 
7.5%
a 20121
 
6.6%
i 9867
 
3.2%
o 8287
 
2.7%
Other values (36) 78910
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 227443
74.9%
Uppercase Letter 42223
 
13.9%
Space Separator 34102
 
11.2%
Other Punctuation 34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 28205
12.4%
s 27729
12.2%
b 25711
11.3%
e 24657
10.8%
d 23484
10.3%
a 20121
8.8%
i 9867
 
4.3%
o 8287
 
3.6%
r 8235
 
3.6%
h 7825
 
3.4%
Other values (14) 43322
19.0%
Uppercase Letter
ValueCountFrequency (%)
R 22729
53.8%
S 6028
 
14.3%
A 3406
 
8.1%
B 2307
 
5.5%
P 2042
 
4.8%
D 1480
 
3.5%
K 809
 
1.9%
C 721
 
1.7%
L 616
 
1.5%
M 566
 
1.3%
Other values (10) 1519
 
3.6%
Space Separator
ValueCountFrequency (%)
34102
100.0%
Other Punctuation
ValueCountFrequency (%)
& 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 269666
88.8%
Common 34136
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 28205
 
10.5%
s 27729
 
10.3%
b 25711
 
9.5%
e 24657
 
9.1%
d 23484
 
8.7%
R 22729
 
8.4%
a 20121
 
7.5%
i 9867
 
3.7%
o 8287
 
3.1%
r 8235
 
3.1%
Other values (34) 70641
26.2%
Common
ValueCountFrequency (%)
34102
99.9%
& 34
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34102
11.2%
u 28205
 
9.3%
s 27729
 
9.1%
b 25711
 
8.5%
e 24657
 
8.1%
d 23484
 
7.7%
R 22729
 
7.5%
a 20121
 
6.6%
i 9867
 
3.2%
o 8287
 
2.7%
Other values (36) 78910
26.0%
Distinct113
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-05-20T06:29:21.047953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length8
Mean length10.930218
Min length8

Characters and Unicode

Total characters354150
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowSS : 486
2nd rowSS : 429
3rd rowSS : 486
4th rowSS : 486
5th rowSS : 486
ValueCountFrequency (%)
40541
33.3%
ss 31161
25.6%
486 10073
 
8.3%
524 5739
 
4.7%
lb 4690
 
3.9%
ub 4690
 
3.9%
1500 3786
 
3.1%
1000 1833
 
1.5%
1150 1605
 
1.3%
400 1523
 
1.3%
Other values (77) 15982
 
13.1%
2025-05-20T06:29:21.520876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89222
25.2%
S 62322
17.6%
0 41528
11.7%
: 40541
11.4%
4 19330
 
5.5%
5 18294
 
5.2%
1 14779
 
4.2%
6 12750
 
3.6%
8 11550
 
3.3%
B 9380
 
2.6%
Other values (8) 34454
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134726
38.0%
Space Separator 89222
25.2%
Uppercase Letter 81082
22.9%
Other Punctuation 49120
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41528
30.8%
4 19330
14.3%
5 18294
13.6%
1 14779
 
11.0%
6 12750
 
9.5%
8 11550
 
8.6%
2 8951
 
6.6%
9 3353
 
2.5%
7 2833
 
2.1%
3 1358
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
S 62322
76.9%
B 9380
 
11.6%
U 4690
 
5.8%
L 4690
 
5.8%
Other Punctuation
ValueCountFrequency (%)
: 40541
82.5%
, 8140
 
16.6%
. 439
 
0.9%
Space Separator
ValueCountFrequency (%)
89222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 273068
77.1%
Latin 81082
 
22.9%

Most frequent character per script

Common
ValueCountFrequency (%)
89222
32.7%
0 41528
15.2%
: 40541
14.8%
4 19330
 
7.1%
5 18294
 
6.7%
1 14779
 
5.4%
6 12750
 
4.7%
8 11550
 
4.2%
2 8951
 
3.3%
, 8140
 
3.0%
Other values (4) 7983
 
2.9%
Latin
ValueCountFrequency (%)
S 62322
76.9%
B 9380
 
11.6%
U 4690
 
5.8%
L 4690
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89222
25.2%
S 62322
17.6%
0 41528
11.7%
: 40541
11.4%
4 19330
 
5.5%
5 18294
 
5.2%
1 14779
 
4.2%
6 12750
 
3.6%
8 11550
 
3.3%
B 9380
 
2.6%
Other values (8) 34454
 
9.7%

E Ticket
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047035585
Minimum0
Maximum15
Zeros31468
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:21.676937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34899847
Coefficient of variation (CV)7.4198815
Kurtosis300.95413
Mean0.047035585
Median Absolute Deviation (MAD)0
Skewness13.652435
Sum1524
Variance0.12179993
MonotonicityNot monotonic
2025-05-20T06:29:21.848873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 31468
97.1%
1 607
 
1.9%
2 206
 
0.6%
3 60
 
0.2%
4 32
 
0.1%
5 9
 
< 0.1%
6 6
 
< 0.1%
9 4
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 31468
97.1%
1 607
 
1.9%
2 206
 
0.6%
3 60
 
0.2%
4 32
 
0.1%
5 9
 
< 0.1%
6 6
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 6
 
< 0.1%
5 9
 
< 0.1%
4 32
0.1%
3 60
0.2%

Agents
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4412518
Minimum0
Maximum40
Zeros1268
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:22.044852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0632239
Coefficient of variation (CV)0.73770866
Kurtosis106.60909
Mean1.4412518
Median Absolute Deviation (MAD)0
Skewness5.8547699
Sum46698
Variance1.1304451
MonotonicityNot monotonic
2025-05-20T06:29:22.184387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 21331
65.8%
2 6789
 
21.0%
3 1660
 
5.1%
0 1268
 
3.9%
4 733
 
2.3%
6 302
 
0.9%
5 234
 
0.7%
7 22
 
0.1%
8 18
 
0.1%
9 12
 
< 0.1%
Other values (15) 32
 
0.1%
ValueCountFrequency (%)
0 1268
 
3.9%
1 21331
65.8%
2 6789
 
21.0%
3 1660
 
5.1%
4 733
 
2.3%
5 234
 
0.7%
6 302
 
0.9%
7 22
 
0.1%
8 18
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
40 1
< 0.1%
29 1
< 0.1%
27 1
< 0.1%
23 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
18 1
< 0.1%
17 1
< 0.1%
16 2
< 0.1%
15 1
< 0.1%

Seats
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5235641
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:22.294004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum40
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1218957
Coefficient of variation (CV)0.73636266
Kurtosis109.82211
Mean1.5235641
Median Absolute Deviation (MAD)0
Skewness6.615549
Sum49365
Variance1.25865
MonotonicityNot monotonic
2025-05-20T06:29:22.424540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 22045
68.0%
2 7066
 
21.8%
3 1779
 
5.5%
4 787
 
2.4%
6 316
 
1.0%
5 271
 
0.8%
7 35
 
0.1%
8 25
 
0.1%
9 23
 
0.1%
10 11
 
< 0.1%
Other values (16) 43
 
0.1%
ValueCountFrequency (%)
1 22045
68.0%
2 7066
 
21.8%
3 1779
 
5.5%
4 787
 
2.4%
5 271
 
0.8%
6 316
 
1.0%
7 35
 
0.1%
8 25
 
0.1%
9 23
 
0.1%
10 11
 
< 0.1%
ValueCountFrequency (%)
40 1
< 0.1%
29 1
< 0.1%
27 2
< 0.1%
25 1
< 0.1%
23 1
< 0.1%
22 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
18 2
< 0.1%
17 2
< 0.1%

Seating Capacity
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.636863
Minimum37
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:22.526310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile42
Q145
median45
Q349
95-th percentile61
Maximum61
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3544412
Coefficient of variation (CV)0.11240121
Kurtosis1.0869859
Mean47.636863
Median Absolute Deviation (MAD)0
Skewness1.2654441
Sum1543482
Variance28.670041
MonotonicityNot monotonic
2025-05-20T06:29:22.609832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
45 18134
56.0%
49 4716
 
14.6%
53 3001
 
9.3%
60 1826
 
5.6%
61 1624
 
5.0%
42 1240
 
3.8%
44 1113
 
3.4%
37 747
 
2.3%
ValueCountFrequency (%)
37 747
 
2.3%
42 1240
 
3.8%
44 1113
 
3.4%
45 18134
56.0%
49 4716
 
14.6%
53 3001
 
9.3%
60 1826
 
5.6%
61 1624
 
5.0%
ValueCountFrequency (%)
61 1624
 
5.0%
60 1826
 
5.6%
53 3001
 
9.3%
49 4716
 
14.6%
45 18134
56.0%
44 1113
 
3.4%
42 1240
 
3.8%
37 747
 
2.3%

Cash Amount
Real number (ℝ)

Skewed  Zeros 

Distinct119
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.785808
Minimum0
Maximum35910
Zeros32066
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:22.734565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35910
Range35910
Interquartile range (IQR)0

Descriptive statistics

Standard deviation413.63567
Coefficient of variation (CV)15.442345
Kurtosis2566.5894
Mean26.785808
Median Absolute Deviation (MAD)0
Skewness39.871534
Sum867886.95
Variance171094.47
MonotonicityNot monotonic
2025-05-20T06:29:22.887013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32066
99.0%
550.2 13
 
< 0.1%
651 12
 
< 0.1%
510.3 10
 
< 0.1%
2110.5 9
 
< 0.1%
530.25 8
 
< 0.1%
2751 8
 
< 0.1%
1650.6 8
 
< 0.1%
2520 7
 
< 0.1%
1100.4 7
 
< 0.1%
Other values (109) 253
 
0.8%
ValueCountFrequency (%)
0 32066
99.0%
367.5 1
 
< 0.1%
510.3 10
 
< 0.1%
530.25 8
 
< 0.1%
550.2 13
 
< 0.1%
577.5 1
 
< 0.1%
628.95 4
 
< 0.1%
651 12
 
< 0.1%
682.5 1
 
< 0.1%
703.5 4
 
< 0.1%
ValueCountFrequency (%)
35910 1
< 0.1%
26565 1
< 0.1%
17325 1
< 0.1%
13860 1
< 0.1%
13755 1
< 0.1%
12206.25 1
< 0.1%
11550 1
< 0.1%
11025 1
< 0.1%
10080 1
< 0.1%
9353.4 1
< 0.1%

E Ticket Amount
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct163
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.268045
Minimum0
Maximum31421.25
Zeros31471
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:23.033035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum31421.25
Range31421.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation379.93047
Coefficient of variation (CV)9.9281389
Kurtosis1793.3686
Mean38.268045
Median Absolute Deviation (MAD)0
Skewness31.356613
Sum1239922.9
Variance144347.16
MonotonicityNot monotonic
2025-05-20T06:29:23.190596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31471
97.1%
510.3 133
 
0.4%
550.2 108
 
0.3%
484.79 47
 
0.1%
1020.6 40
 
0.1%
1496.25 39
 
0.1%
1100.4 34
 
0.1%
522.69 33
 
0.1%
450.45 27
 
0.1%
798 19
 
0.1%
Other values (153) 450
 
1.4%
ValueCountFrequency (%)
0 31471
97.1%
349.13 1
 
< 0.1%
380.05 2
 
< 0.1%
399 6
 
< 0.1%
420 1
 
< 0.1%
427.93 8
 
< 0.1%
448.88 1
 
< 0.1%
450.45 27
 
0.1%
473.81 2
 
< 0.1%
484.79 47
 
0.1%
ValueCountFrequency (%)
31421.25 1
< 0.1%
14962.5 1
< 0.1%
13965 1
< 0.1%
12468.75 1
< 0.1%
11970 1
< 0.1%
11371.5 2
< 0.1%
9975 1
< 0.1%
9576 1
< 0.1%
9177.04 1
< 0.1%
8079.75 1
< 0.1%

Agent Amount
Real number (ℝ)

High correlation  Zeros 

Distinct690
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.4928
Minimum0
Maximum54500
Zeros1299
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:23.341098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile386.1
Q1450
median675
Q31092.5
95-th percentile2774
Maximum54500
Range54500
Interquartile range (IQR)642.5

Descriptive statistics

Standard deviation1226.704
Coefficient of variation (CV)1.2056144
Kurtosis277.74555
Mean1017.4928
Median Absolute Deviation (MAD)237.6
Skewness10.352149
Sum32967785
Variance1504802.8
MonotonicityNot monotonic
2025-05-20T06:29:23.484163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.4 5419
 
16.7%
471.6 3070
 
9.5%
0 1299
 
4.0%
874.8 1293
 
4.0%
461.7 1111
 
3.4%
943.2 924
 
2.9%
500.3 843
 
2.6%
386.1 794
 
2.5%
1080 703
 
2.2%
1350 682
 
2.1%
Other values (680) 16263
50.2%
ValueCountFrequency (%)
0 1299
4.0%
280.8 2
 
< 0.1%
297.9 4
 
< 0.1%
307.8 2
 
< 0.1%
315 2
 
< 0.1%
342.9 95
 
0.3%
360 145
 
0.4%
380 18
 
0.1%
386.1 794
2.5%
390.05 7
 
< 0.1%
ValueCountFrequency (%)
54500 1
< 0.1%
50000 1
< 0.1%
36585 1
< 0.1%
35815 1
< 0.1%
29640 1
< 0.1%
28455 1
< 0.1%
24035 1
< 0.1%
22990 1
< 0.1%
18780 1
< 0.1%
18700 1
< 0.1%

Base Fare
Real number (ℝ)

High correlation 

Distinct453
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.1593
Minimum0
Maximum52000
Zeros34
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:24.100248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile486
Q1486
median800
Q31250
95-th percentile3100
Maximum52000
Range52000
Interquartile range (IQR)764

Descriptive statistics

Standard deviation1339.7437
Coefficient of variation (CV)1.1537984
Kurtosis197.73292
Mean1161.1593
Median Absolute Deviation (MAD)314
Skewness9.0963783
Sum37622723
Variance1794913.3
MonotonicityNot monotonic
2025-05-20T06:29:24.257603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
486 7516
23.2%
524 4052
 
12.5%
972 1883
 
5.8%
1048 1199
 
3.7%
429 1015
 
3.1%
1200 897
 
2.8%
1150 856
 
2.6%
1500 828
 
2.6%
1000 776
 
2.4%
800 745
 
2.3%
Other values (443) 12634
39.0%
ValueCountFrequency (%)
0 34
 
0.1%
312 2
 
< 0.1%
331 4
 
< 0.1%
332.5 1
 
< 0.1%
342 2
 
< 0.1%
350 3
 
< 0.1%
361.95 2
 
< 0.1%
380 6
 
< 0.1%
381 102
0.3%
390 1
 
< 0.1%
ValueCountFrequency (%)
52000 1
< 0.1%
48000 1
< 0.1%
37700 1
< 0.1%
35100 1
< 0.1%
34200 1
< 0.1%
31200 1
< 0.1%
29925 1
< 0.1%
27300 1
< 0.1%
25300 2
< 0.1%
22000 1
< 0.1%

GST
Real number (ℝ)

Zeros 

Distinct369
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.889536
Minimum0
Maximum2600
Zeros23897
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:24.393572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.3
95-th percentile78.6
Maximum2600
Range2600
Interquartile range (IQR)24.3

Descriptive statistics

Standard deviation56.005268
Coefficient of variation (CV)3.3159744
Kurtosis431.9725
Mean16.889536
Median Absolute Deviation (MAD)0
Skewness14.832083
Sum547237.84
Variance3136.59
MonotonicityNot monotonic
2025-05-20T06:29:24.532460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23897
73.8%
24.3 2097
 
6.5%
26.2 982
 
3.0%
48.6 590
 
1.8%
52.4 275
 
0.8%
25.25 264
 
0.8%
21.45 221
 
0.7%
60 188
 
0.6%
57.5 188
 
0.6%
50 186
 
0.6%
Other values (359) 3513
 
10.8%
ValueCountFrequency (%)
0 23897
73.8%
16.63 1
 
< 0.1%
17.5 1
 
< 0.1%
18.1 2
 
< 0.1%
19 6
 
< 0.1%
19.05 7
 
< 0.1%
19.5 1
 
< 0.1%
19.9 1
 
< 0.1%
20 77
 
0.2%
20.38 8
 
< 0.1%
ValueCountFrequency (%)
2600 1
< 0.1%
2400 1
< 0.1%
1885 1
< 0.1%
1755 1
< 0.1%
1710 1
< 0.1%
1560 1
< 0.1%
1496.25 1
< 0.1%
1365 1
< 0.1%
1265 2
< 0.1%
1100 1
< 0.1%

Amount
Real number (ℝ)

High correlation 

Distinct626
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1178.0489
Minimum0
Maximum54600
Zeros34
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:24.666442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile486
Q1510.3
median800
Q31260
95-th percentile3150
Maximum54600
Range54600
Interquartile range (IQR)749.7

Descriptive statistics

Standard deviation1376.8442
Coefficient of variation (CV)1.1687496
Kurtosis215.06188
Mean1178.0489
Median Absolute Deviation (MAD)314
Skewness9.5206924
Sum38169961
Variance1895699.9
MonotonicityNot monotonic
2025-05-20T06:29:24.823350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
486 5419
 
16.7%
524 3070
 
9.5%
510.3 2097
 
6.5%
972 1293
 
4.0%
550.2 982
 
3.0%
1048 924
 
2.9%
429 794
 
2.5%
1200 709
 
2.2%
1500 683
 
2.1%
1150 668
 
2.1%
Other values (616) 15762
48.6%
ValueCountFrequency (%)
0 34
 
0.1%
312 2
 
< 0.1%
331 4
 
< 0.1%
342 2
 
< 0.1%
349.13 1
 
< 0.1%
350 2
 
< 0.1%
367.5 1
 
< 0.1%
380.05 2
 
< 0.1%
381 95
0.3%
399 6
 
< 0.1%
ValueCountFrequency (%)
54600 1
< 0.1%
50400 1
< 0.1%
39585 1
< 0.1%
36855 1
< 0.1%
35910 1
< 0.1%
32760 1
< 0.1%
31421.25 1
< 0.1%
28665 1
< 0.1%
26565 2
< 0.1%
23100 1
< 0.1%

Agent Commission
Real number (ℝ)

High correlation  Zeros 

Distinct295
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.502177
Minimum0
Maximum3770
Zeros1299
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:24.952338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q148.6
median52.4
Q3110
95-th percentile285
Maximum3770
Range3770
Interquartile range (IQR)61.4

Descriptive statistics

Standard deviation111.68932
Coefficient of variation (CV)1.169495
Kurtosis87.681457
Mean95.502177
Median Absolute Deviation (MAD)42.4
Skewness5.9140016
Sum3094366
Variance12474.505
MonotonicityNot monotonic
2025-05-20T06:29:25.091717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.6 6530
20.2%
52.4 3570
 
11.0%
10 2405
 
7.4%
97.2 1600
 
4.9%
0 1299
 
4.0%
104.8 1054
 
3.3%
20 989
 
3.1%
42.9 889
 
2.7%
120 787
 
2.4%
115 735
 
2.3%
Other values (285) 12543
38.7%
ValueCountFrequency (%)
0 1299
4.0%
10 2405
7.4%
20 989
3.1%
30 274
 
0.8%
30.3 1
 
< 0.1%
31.2 2
 
< 0.1%
31.44 1
 
< 0.1%
33.1 4
 
< 0.1%
34.2 2
 
< 0.1%
35 2
 
< 0.1%
ValueCountFrequency (%)
3770 1
 
< 0.1%
3120 1
 
< 0.1%
2530 1
 
< 0.1%
1890 1
 
< 0.1%
1800 1
 
< 0.1%
1750 3
< 0.1%
1620 1
 
< 0.1%
1600 1
 
< 0.1%
1540 1
 
< 0.1%
1500 2
< 0.1%

Net Amount
Real number (ℝ)

High correlation 

Distinct921
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1082.5467
Minimum0
Maximum54500
Zeros34
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size253.3 KiB
2025-05-20T06:29:25.253704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile437.4
Q1461.7
median725
Q31170
95-th percentile2970
Maximum54500
Range54500
Interquartile range (IQR)708.3

Descriptive statistics

Standard deviation1298.3861
Coefficient of variation (CV)1.1993812
Kurtosis252.49968
Mean1082.5467
Median Absolute Deviation (MAD)287.6
Skewness10.318074
Sum35075595
Variance1685806.6
MonotonicityNot monotonic
2025-05-20T06:29:25.416003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.4 5419
 
16.7%
471.6 3070
 
9.5%
874.8 1293
 
4.0%
461.7 1111
 
3.4%
943.2 924
 
2.9%
500.3 843
 
2.6%
386.1 794
 
2.5%
1080 703
 
2.2%
1350 682
 
2.1%
1035 666
 
2.1%
Other values (911) 16896
52.1%
ValueCountFrequency (%)
0 34
 
0.1%
280.8 2
 
< 0.1%
297.9 4
 
< 0.1%
307.8 2
 
< 0.1%
315 2
 
< 0.1%
342.9 95
0.3%
349.13 1
 
< 0.1%
360 145
0.4%
367.5 1
 
< 0.1%
380 18
 
0.1%
ValueCountFrequency (%)
54500 1
< 0.1%
50000 1
< 0.1%
36585 1
< 0.1%
35910 1
< 0.1%
35815 1
< 0.1%
31421.25 1
< 0.1%
29640 1
< 0.1%
28455 1
< 0.1%
26565 1
< 0.1%
24035 1
< 0.1%

Interactions

2025-05-20T06:29:15.093306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:56.471363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.983502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.442445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.253039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.608851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.133046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.645203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.846198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.729864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.134103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.980485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.212495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:56.623516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.109943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.566197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.379794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.731593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.252950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.077236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.998944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.837282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.269846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:13.096940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.351603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:56.767209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.233393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.687607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.489025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.849674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.387944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.216044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:08.167969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.953140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.394777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:13.216138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.482004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:56.906079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.363962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.803040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.599546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.969324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.518414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.353338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:08.364120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.064157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.532755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:13.353682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.608210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.019891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.474334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.931983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.707016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.094861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.642469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.498501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:08.539195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.169052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.646313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:13.460685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.742928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.138189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.604155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:00.050015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.825421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.233833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.767786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.675207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:08.719369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.291428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.765530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.168229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.856710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.255316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.724942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:00.181056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.940472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.357691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:04.891247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:06.854952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:08.908760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.406415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.031438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.285239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:15.973153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.379060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.835819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:00.397569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.062180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.480625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.013795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.019522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.091163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.541382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.193466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.405961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:16.089672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.494428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:58.959880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:00.769629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.166203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.609521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.152120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.184320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.258933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.663969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.305215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.537766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:16.217989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.615169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.080872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:00.883076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.285686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.729294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.270426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.363340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.387140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.775413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.421294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.688663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:16.336430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.727737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.198074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.012589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.391951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.853338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.396615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.517195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.508821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:10.892708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.546099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.826643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:16.456931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:57.868108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:28:59.326082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:01.141057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:02.502821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:03.982220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:05.524550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:07.694712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:09.625717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:11.019214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:12.699358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T06:29:14.958599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-20T06:29:25.547427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Agent AmountAgent CommissionAgentsAmountBase FareCash AmountCoach NoCoach TypeDestinationE TicketE Ticket AmountGSTNet AmountOriginPlace Of SupplyRouteSeating CapacitySeats
Agent Amount1.0000.7260.7030.8930.886-0.1700.0550.0480.049-0.287-0.2870.0520.8850.0650.0650.0750.1520.582
Agent Commission0.7261.0000.5520.6470.679-0.1710.0760.0600.053-0.288-0.287-0.4580.6000.0910.0910.1020.1100.421
Agents0.7030.5521.0000.5630.571-0.2030.0430.0400.052-0.341-0.341-0.0490.5490.0540.0540.066-0.0600.866
Amount0.8930.6470.5631.0000.9930.0980.0640.0500.0550.0220.0240.1780.9930.0710.0710.0800.1680.663
Base Fare0.8860.6790.5710.9931.0000.0920.0640.0500.0550.0030.0060.0970.9790.0730.0730.0820.1690.664
Cash Amount-0.170-0.171-0.2030.0980.0921.0000.0270.0200.000-0.018-0.0180.1990.1060.0000.0000.0620.0560.099
Coach No0.0550.0760.0430.0640.0640.0271.0000.9990.2720.0240.0310.0360.0590.2720.2720.4570.9990.044
Coach Type0.0480.0600.0400.0500.0500.0200.9991.0000.3610.0220.0320.0330.0500.3780.3780.7020.8710.038
Destination0.0490.0530.0520.0550.0550.0000.2720.3611.0000.0330.0160.0250.0490.1940.1940.4910.3690.050
E Ticket-0.287-0.288-0.3410.0220.003-0.0180.0240.0220.0331.0000.9980.2840.0660.0120.0120.054-0.0210.016
E Ticket Amount-0.287-0.287-0.3410.0240.006-0.0180.0310.0320.0160.9981.0000.2850.0680.0140.0140.043-0.0210.014
GST0.052-0.458-0.0490.1780.0970.1990.0360.0330.0250.2840.2851.0000.2240.0270.0270.0520.0550.128
Net Amount0.8850.6000.5490.9930.9790.1060.0590.0500.0490.0660.0680.2241.0000.0670.0670.0780.1690.664
Origin0.0650.0910.0540.0710.0730.0000.2720.3780.1940.0120.0140.0270.0671.0001.0000.4630.3990.053
Place Of Supply0.0650.0910.0540.0710.0730.0000.2720.3780.1940.0120.0140.0270.0671.0001.0000.4630.3990.053
Route0.0750.1020.0660.0800.0820.0620.4570.7020.4910.0540.0430.0520.0780.4630.4631.0000.7370.071
Seating Capacity0.1520.110-0.0600.1680.1690.0560.9990.8710.369-0.021-0.0210.0550.1690.3990.3990.7371.000-0.056
Seats0.5820.4210.8660.6630.6640.0990.0440.0380.0500.0160.0140.1280.6640.0530.0530.071-0.0561.000

Missing values

2025-05-20T06:29:16.714453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-20T06:29:17.084251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DojHubRouteService NoOriginDestinationPlace Of SupplyDept TimeCoach NoCoach TypeCategoryBooked ByService FareE TicketAgentsSeatsSeating CapacityCash AmountE Ticket AmountAgent AmountBase FareGSTAmountAgent CommissionNet Amount
02023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 486022450.00.0874.8972.00.0972.097.2874.8
12023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401BurdwanSaltlakeBurdwan - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 429022450.00.0772.2858.00.0858.085.8772.2
22023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:55:00NaNVolvo SeaterACBhairab SinghaSS : 486022450.00.0923.4972.048.61020.697.2923.4
32023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 486022450.00.0874.8972.00.0972.097.2874.8
42023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.4
52023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401BurdwanKolkataBurdwan - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 429011450.00.0386.1429.00.0429.042.9386.1
62023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401AsansolKolkataAsansol - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 524011450.00.0471.6524.00.0524.052.4471.6
72023-01-01KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:55:00NaNVolvo SeaterACRedbusSS : 486044450.00.01749.61944.00.01944.0194.41749.6
82023-01-01KolkataAsansol To KolkataAE - 304Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal06:00:00WB 37 C 3388Volvo SeaterACSystem AdministratorSS : 486202450.01020.60.0972.048.61020.60.01020.6
92023-01-01KolkataAsansol To KolkataAE - 304AsansolKolkataAsansol - West Bengal06:00:00WB 37 C 3388Volvo SeaterACRedbusSS : 524033450.00.01414.81572.00.01572.0157.21414.8
DojHubRouteService NoOriginDestinationPlace Of SupplyDept TimeCoach NoCoach TypeCategoryBooked ByService FareE TicketAgentsSeatsSeating CapacityCash AmountE Ticket AmountAgent AmountBase FareGSTAmountAgent CommissionNet Amount
323912023-01-31KolkataBokaro To KolkataBK - 601BokaroKolkataBokaro - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 670011490.00.0603.0670.00.0670.067.0603.0
323922023-01-31KolkataBokaro To KolkataBK - 601DhanbadKolkataDhanbad - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 620011490.00.0558.0620.00.0620.062.0558.0
323932023-01-31KolkataBokaro To KolkataBK - 601DhanbadKolkataDhanbad - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACPappu DhanbadSS : 620011490.00.0641.0620.031.0651.010.0641.0
323942023-01-31KolkataBokaro To KolkataBK - 601Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACAnupamdSS : 486011490.00.0500.3486.024.3510.310.0500.3
323952023-01-31KolkataBokaro To KolkataBK - 601Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACBhairab SinghaSS : 486011490.00.0500.3486.024.3510.310.0500.3
323962023-01-31KolkataBokaro To KolkataBK - 601Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 486011490.00.0437.4486.00.0486.048.6437.4
323972023-01-31KolkataBokaro To KolkataBK - 601Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West Bengal05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 486033490.00.01312.21458.00.01458.0145.81312.2
323982023-01-31KolkataBokaro To KolkataBK - 601BokaroRaniganjBokaro - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 429011490.00.0386.1429.00.0429.042.9386.1
323992023-01-31KolkataBokaro To KolkataBK - 601DhanbadKolkataDhanbad - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 620011490.00.0558.0620.00.0620.062.0558.0
324002023-01-31KolkataBokaro To KolkataBK - 601BokaroKolkataBokaro - Jharkhand05:20:00WB 23 E 3888Multi Axle Volvo SemiSleeperACRedbusSS : 670022490.00.01206.01340.00.01340.0134.01206.0

Duplicate rows

Most frequently occurring

DojHubRouteService NoOriginDestinationPlace Of SupplyCoach NoCoach TypeCategoryBooked ByService FareE TicketAgentsSeatsSeating CapacityCash AmountE Ticket AmountAgent AmountBase FareGSTAmountAgent CommissionNet Amount# duplicates
50692023-01-30KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West BengalWB 37 D 0092Volvo SeaterACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.423
38032023-01-22KolkataKolkata - Siliguri - 905KS - 905KolkataSiliguriKolkata - West BengalWB 37 E 3359Scania Multi-Axle Semi SleeperACRedbusSS : 1150011530.00.01035.01150.00.01150.0115.01035.020
44212023-01-25KolkataSliguri - Kolkata - 1007SK - 1007SiliguriKolkataSiliguri - West BengalWB 37 E 3359Scania Multi-Axle Semi SleeperACRedbusSS : 1300011530.00.01170.01300.00.01300.0130.01170.020
50952023-01-30KolkataAsansol - Kolkata (Karunamoyee)AS - FWDDurgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West BengalWB 23 C 0506Volvo SeaterACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.419
4042023-01-03KolkataAsansol - Kolkata (Karunamoyee)AS - 401Durgapur ( West Bengal )KolkataDurgapur ( West Bengal ) - West BengalWB 23 D 5904Scania Metro LinkACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.418
27012023-01-16KolkataKolkata To AsansolEA - 102KolkataDurgapur ( West Bengal )Kolkata - West BengalWB 37 E 3415Scania Multi-Axle Semi SleeperACRedbusSS : 486011530.00.0437.4486.00.0486.048.6437.418
46362023-01-27KolkataKolkata (Karunamoyee) - AsansolSA - 202KolkataDurgapur ( West Bengal )Kolkata - West BengalWB 41 F 0307Volvo SeaterACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.418
51862023-01-30KolkataKolkata To AsansolEA - 102KolkataDurgapur ( West Bengal )Kolkata - West BengalWB 23 E 1402Volvo SeaterACRedbusSS : 486011450.00.0437.4486.00.0486.048.6437.418
14812023-01-09KolkataKolkata (Karunamoyee) - AsansolSA - 203KolkataDurgapur ( West Bengal )Kolkata - West BengalWB 23 E 3411Multi Axle Volvo SemiSleeperACRedbusSS : 486011490.00.0437.4486.00.0486.048.6437.417
21842023-01-13KolkataPuri - Kolkata - 1201PK - 1201BhubaneswarKolkataBhubaneswar - OrissaWB 37 D 0092Volvo SeaterACRedbusSS : 800011450.00.0900.01000.00.01000.0100.0900.017